Zicheng Zhang

Most existing visual CAPTCHAs remain spatially solvable: the required information is exposed by static appearance, local structure, and interface state. This assumption is weakened by advances in multimodal large language models (MLLMs) and Graphical User Interface (GUI) agents, which exhibit strong visual perception, reasoning, and browser interaction capabilities. We propose Motion Vision CAPTCHA (MVCAP), a hierarchical motion-based CAPTCHA framework in which target semantics are instantiated as motion-defined foreground structures and become recoverable only through temporal segregation from a dynamically evolving background. Built on this shared principle, MVCAP is instantiated in three perceptually progressive levels: coherent motion, structural motion, and biological motion. To evaluate this framework, we introduce MVCAP-Bench, a browser-based benchmark with 600 live CAPTCHA instances, together with a matched foreground-only control benchmark, MVCAP-Bench-FG. We evaluate humans, Browser Use agents, native computer use agents, and a supplementary offline VQA setting derived from the same instances. Results reveal a substantial human–agent gap: on the full MVCAP-Bench, human accuracy reaches 99.6%, whereas the best GUI agent achieves only 16.8%, close to the six-way chance level. The foreground-only control further shows that the key difficulty comes from dynamic background camouflage rather than answer format or browser interaction alone. These findings identify a measurable human–agent perception gap and position MVCAP-Bench as a benchmark for studying motion-defined perception in current agents.

Although recent advances in AI-based Text-to-Motion (T2M) algorithms have enabled text-driven 3D Animated Human (3DAH), often referred to as "Puppeteers" for their ability to streamline 3D animation design, the quality of the generated motion remains a critical concern. More importantly, the T2M field continues to lack reliable and objective evaluation metrics, which substantially hinders the advancement of puppeteers and negatively impacts user experience. To address this gap, we introduce the first large-scale quality assessment dataset for generative 3DAHs, termed AHQA-3D, which comprises 1,200 3DAHs sequences generated by 6 representative puppeteers. Through carefully designed subjective experiments involving recruited participants, we obtain human quality ratings for each 3DAH. The results highlight the impact of different factors on animation quality. We further propose ViSCA, an objective quality assessment agent based on Qwen2.5-VL. ViSCA supports end-to-end quality evaluation across four dimensions: 3D motion fidelity, 2D human body representation, visual quality, and semantic consistency. Experimental results demonstrate that ViSCA outperforms existing evaluation methods and exhibits stronger alignment with human perception.

Web3 technologies enable novel forms of real-time digital human streaming media by supporting both high-fidelity transmission and interactive user engagement. However, Real-Time Streaming Interactive Digital Humans (RTSIDHs) remain vulnerable to network instability, resulting in buffering, latency, visual degradation, and audio–video desynchronization that substantially impair user Quality of Experience (QoE). To effectively perceive these distortions, we present RDHQA, the first large-scale RTSIDH Quality Assessment dataset. RDHQA comprises 134 representative interaction scenarios with eight digital human avatars as high-quality references, along with 1,340 distorted samples generated by simulating five common streaming degradations. Based on extensive subjective evaluations, we further propose SAV-PF, an audio–visual quality assessment method built on the human foundation model Sapiens and informed by cognitive principles such as the primacy effect and forgetting curve. Experimental results demonstrate that SAV-PF achieves superior performance over existing objective QoE assessment approaches, providing a more accurate prediction of user experience. This work is open-sourced at https://github.com/zyj-2000/RDHQA under the CC BY-NC 4.0 licence.

The rapid advancement of AI generation technologies has led to the widespread use of AI-generated multimedia content, including images, videos, and 3D contents, across various applications. While significant progress has been made in quality evaluation for 2D content, evaluating the quality of 3D content synthesized from single image remains an underexplored problem. To bridge this gap, we introduce the first comprehensive subjective evaluation database tailored for assessing the quality of 3D content generated from single image. Our database, named AIGC-SI23DCQA, includes three distinct categories of input images, i.e., realistic images, AI-generated images, and computer graphic (CG) images, with 100 images in each category. Using five representative single-image-to-3D algorithms, we produce 1,500 3D contents and collect 94,500 annotations across three quality dimensions, including texture fidelity, shape accuracy, and overall quality. Based on the constructed database, we first benchmark and evaluate the performance of existing quality assessment methods revealing their limitations in addressing this novel task. Thus, we further propose a novel objective quality assessment method, termed I3DQA, for effective single-image-to-3D content quality assessment. Specifically, I3DQA first extracts the reference features from the source image, and the multi-modal features from the generated 3D content, including the projected video, patches, and large-multimodal model (LMM) features. These features are integrated through symmetric transformer blocks, enabling effective quality-related feature fusion and score prediction. Extensive experiments demonstrate the superior performance of our method and validate the effectiveness of its components. This work provides a foundational resource and a robust framework for advancing research in this emerging field, and our database and model will be released.